Extracting seizure onset from surface EEG with independent component analysis: Insights from simultaneous scalp and intracerebral EEG.
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ABSTRACT:
SUBMITTER: Barborica A
PROVIDER: S-EPMC8503578 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeCompare the detection rate of seizures on scalp EEG with simultaneous intracranial stereo EEG (SEEG) recordings.MethodsTwenty-seven drug-resistant epilepsy patients undergoing SEEG with simultaneous scalp EEG as part of their surgical work-up were included. A total of 172 seizures were captured.ResultsOf the 172 seizures detected on SEEG, 100 demonstrated scalp ictal patterns. Focal aware and subclinical seizures were less likely to be seen on scalp, with 33% of each observed when compared with focal impaired aware (97%) and focal to bilateral tonic-clonic seizures (100%) (P < 0.001). Of the 72 seizures without ictal scalp correlate, 32 demonstrated an abnormality during the SEEG seizure that was identical to an interictal abnormality. Seizures from patients with MRI lesions were statistically less likely to be seen on scalp than seizures from nonlesional patients (P = 0.0162). Stereo EEG seizures not seen on scalp were shorter in duration (49 seconds) compared with SEEG seizures seen on scalp (108.6 seconds) (P < 0.001).ConclusionsScalp EEG is not a sensitive tool for the detection of focal aware and subclinical seizures but is highly sensitive for the detection of focal impaired aware and focal to bilateral tonic-clonic seizures. Longer duration of seizure and seizures from patients without MRI lesions were more likely to be apparent on scalp. Abnormalities seen interictally may at times represent an underlying seizure. The cognitive, affective, and behavioral long-term effects of ongoing difficult-to-detect seizures are not known.
| S-EPMC8290181 | biostudies-literature
Project description:OBJECTIVE:The purpose of this study was to evaluate and validate an offline, automated scalp EEG-based seizure detection system and to compare its performance to commercially available seizure detection software. METHODS:The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ?3653h with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (?1200h with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persyst's Reveal®, version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms. RESULTS:The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24h, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24h, respectively. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p<0.05) smaller FDR. CONCLUSIONS:The study validates the performance of the IdentEvent™ seizure detection system. SIGNIFICANCE:With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.
| S-EPMC2934863 | biostudies-literature
Project description:Epilepsy is a chronic brain disease that causes persistent and severe damage to the physical and mental health of patients. Daily effective prediction of epileptic seizures is crucial for epilepsy patients especially those with refractory epilepsy. At present, a large number of deep learning algorithms such as Convolutional Neural Networks and Recurrent Neural Networks have been used to predict epileptic seizures and have obtained better performance than traditional machine learning methods. However, these methods usually transform the Electroencephalogram (EEG) signal into a Euclidean grid structure. The conversion suffers from loss of adjacent spatial information, which results in deep learning models requiring more storage and computational consumption in the process of information fusion after information extraction. This study proposes a general Graph Convolutional Networks (GCN) model architecture for predicting seizures to solve the problem of oversized seizure prediction models based on exploring the graph structure of EEG signals. As a graph classification task, the network architecture includes graph convolution layers that extract node features with one-hop neighbors, pooling layers that summarize abstract node features; and fully connected layers that implement classification, resulting in superior prediction performance and smaller network size. The experiment shows that the model has an average sensitivity of 96.51%, an average AUC of 0.92, and a model size of 15.5 k on 18 patients in the CHB-MIT scalp EEG dataset. Compared with traditional deep learning methods, which require a large number of parameters and computational effort and are demanding in terms of storage space and energy consumption, this method is more suitable for implementation on compact, low-power wearable devices as a standard process for building a generic low-consumption graph network model on similar biomedical signals. Furthermore, the edge features of graphs can be used to make a preliminary determination of locations and types of discharge, making it more clinically interpretable.
| S-EPMC9376592 | biostudies-literature
Project description:ObjectiveOur objective was to evaluate the relationship between scalp-EEG and stereoelectroencephalography (SEEG) seizure-onset patterns (SOP) in patients with MRI-negative drug-resistant focal epilepsy.MethodsWe analyzed retrospectively 41 patients without visible lesion on brain MRI who underwent video-EEG followed by SEEG. We defined five types of SOPs on scalp-EEG and eight types on SEEG. We examined how various clinical variables affected scalp-EEG SOPs.ResultsThe most prevalent scalp SOPs were rhythmic sinusoidal activity (56.8%), repetitive epileptiform discharges (22.7%), and paroxysmal fast activity (15.9%). The presence of paroxysmal fast activity on scalp-EEG was always seen without delay from clinical onset and correlated with the presence of low-voltage fast activity in SEEG (sensitivity = 22.6%, specificity = 100%). The main factor explaining the discrepancy between the scalp and SEEG SOPs was the delay between clinical and scalp-EEG onset. There was a correlation between the scalp and SEEG SOPs when the scalp onset was simultaneous with the clinical onset (p = 0.026). A significant delay between clinical and scalp discharge onset was observed in 25% of patients and featured always with a rhythmic sinusoidal activity on scalp, corresponding to similar morphology of the discharge on SEEG. The presence of repetitive epileptiform discharges on scalp was associated with an underlying focal cortical dysplasia (sensitivity = 30%, specificity = 90%). There was no significant association between the scalp SOP and the epileptogenic zone location (deep or superficial), or surgical outcome.SignificanceIn patients with MRI-negative focal epilepsy, scalp SOP could suggest the SEEG SOP and some etiology (focal cortical dysplasia) but has no correlation with surgical prognosis. Scalp SOP correlates with the SEEG SOP in cases of simultaneous EEG and clinical onset; otherwise, scalp SOP reflects the propagation of the SEEG discharge.Plain language summaryWe looked at the correspondence between the electrical activity recorded during the start of focal seizure using scalp and intracerebral electrodes in patients with no visible lesion on MRI. If there is a fast activity on scalp, it reflects similar activity inside the brain. We found a good correspondence between scalp and intracerebral electrical activity for cases without significant delay between clinical and scalp electrical onset (seen in 75% of the cases we studied). Visualizing repetitive epileptic activity on scalp could suggest a particular cause of the epilepsy: a subtype of brain malformation called focal cortical dysplasia.
| S-EPMC10984298 | biostudies-literature
Project description:High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R2?=?0.80, p?<?0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p?=?0.0066, PPV?=?86%, NPV?=?80%, accuracy?=?84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.
| S-EPMC6851354 | biostudies-literature
Project description:In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA-GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coefficient. As it is often of interest to identify all those components which exhibit stochastic volatility features we suggest a test statistic for this problem. We also show that a slightly modified version of the principal volatility component analysis can be seen as an ICA method. Finally, we apply the estimators in analysing a data set which consists of time series of exchange rates of seven currencies to US dollar. Supporting information including proofs of the theorems is available online.
| S-EPMC7266430 | biostudies-literature
Project description:ObjectiveThis study aims to identify if oscillations at frequencies higher than the traditional EEG can be recorded on the scalp EEG of patients with focal epilepsy and to analyze the association of these oscillations with interictal discharges and the seizure onset zone (SOZ).MethodsThe scalp EEG of 15 patients with focal epilepsy was studied. We analyzed the rates of gamma (40-80 Hz) and ripple (>80 Hz) oscillations, their co-occurrence with spikes, the number of channels with fast oscillations inside and outside the SOZ, and the specificity, sensitivity, and accuracy of gamma, ripples, and spikes to determine the SOZ.ResultsGamma and ripples frequently co-occurred with spikes (77.5% and 63% of cases). For all events, the proportion of channels with events was consistently higher inside than outside the SOZ: spikes (100% vs 70%), gamma (82% vs 33%), and ripples (48% vs 11%); p < 0.0001. The mean rates (events/min) were higher inside than outside the SOZ: spikes (2.64 ± 1.70 vs 0.69 ± 0.26, p = 0.02), gamma (0.77 ± 0.71 vs 0.20 ± 0.25, p = 0.02), and ripples (0.08 ± 0.12 vs 0.04 ± 0.09, p = 0.04). The sensitivity to identify the SOZ was spikes 100%, gamma 82%, and ripples 48%; the specificity was spikes 30%, gamma 68%, and ripples 89%; and the accuracy was spikes 43%, gamma 70%, and ripples 81%.ConclusionThe rates and the proportion of channels with gamma and ripple fast oscillations are higher inside the SOZ, indicating that they can be used as interictal scalp EEG markers for the SOZ. These fast oscillations are less sensitive but much more specific and accurate than spikes to delineate the SOZ.
| S-EPMC3149155 | biostudies-literature
Project description:ObjectiveAnterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug-resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL.MethodsWe analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure-free and non-seizure-free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave-one-patient-out cross-validation scheme within the MC data set and then tested using the out-of-sample CC data set. Finally, we compared the predictive performance of normal scalp EEG-derived features against MRI abnormalities.ResultsWe found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10-25 Hz range. The Naïve Bayes classification based on those features predicted 1-year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10-25 Hz range provided better predictability than other combinations and (b) normal scalp EEG-derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score).SignificanceThese results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug-resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.
| S-EPMC9283304 | biostudies-literature
Project description:Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many remain in need of new therapeutic approaches. Predicting the impending seizures in these patients could significantly enhance their quality of life if the prediction performance is clinically practical. In this study, we investigate the improvement of the performance of a seizure prediction algorithm in 17 patients with mesial temporal lobe epilepsy by means of a novel measure. Scale-free dynamics of the intracerebral EEG are quantified through robust estimates of the scaling exponents--the first cumulants--derived from a wavelet leader and bootstrap based multifractal analysis. The cumulants are investigated for the discriminability between preictal and interictal epochs. The performance of our recently published patient-specific seizure prediction algorithm is then out-of-sample tested on long-lasting data using combinations of cumulants and state similarity measures previously introduced. By using the first cumulant in combination with state similarity measures, up to 13 of 17 patients had seizures predicted above chance with clinically practical levels of sensitivity (80.5%) and specificity (25.1% of total time under warning) for prediction horizons above 25 min. These results indicate that the scale-free dynamics of the preictal state are different from those of the interictal state. Quantifiers of these dynamics may carry a predictive power that can be used to improve seizure prediction performance.
| S-EPMC4395084 | biostudies-literature
Project description:ObjectiveThe goal of this study is to investigate the performance, merits and limitations of source imaging using intracranial EEG (iEEG) recordings and to compare its accuracy to the results of EEG source imaging. Accuracy in this study, is measured both by determining the location and inter-nodal connectivity of underlying brain networks.MethodsSystematic computer simulation studies are conducted to evaluate iEEG-based source imaging vs. EEG-based source imaging, and source imaging using both EEG and iEEG. To test the source imaging models, networks of inter-connected nodes (in terms of activity) are simulated. The location of the network nodes is randomly selected within a realistic geometry head model and a connectivity link is created among these nodes based on a multi-variate auto-regressive (MVAR) model. Then the forward problem is solved to calculate the potentials at the electrodes and noise (white and correlated) is added to these simulated potentials to simulate realistic measurements. Subsequently, the inverse problem is solved and an algorithm based on principle component analysis is performed on the estimated source activities to determine the location of the simulated network nodes. The activity of these nodes (over time), is then extracted, and used to estimate the connectivity links among the mentioned nodes using Granger causality analysis.ResultsSource imaging based on iEEG recordings may or may not improve the accuracy in localization, depending on the number and location of active nodes relative to iEEG electrodes and to other nodes within the network. However, our simulation results suggest that combining EEG and iEEG modalities (simultaneous scalp and intracranial recordings) can improve the imaging accuracy significantly.ConclusionsWhile iEEG source imaging is useful in estimating the exact location of sources near the iEEG electrodes, combining EEG and iEEG recordings can achieve a more accurate imaging due to the high spatial coverage of the scalp electrodes and the added near field information provided by the iEEG electrodes.SignificanceThe present results suggest the feasibility of localizing brain electrical sources from iEEG recordings and improving EEG source localization using simultaneous EEG and iEEG recordings to cover the whole brain. The hybrid EEG and iEEG source imaging can assist the clinicians when unequivocal decisions about determining the epileptogenic zone cannot be reached using a single modality.
| S-EPMC5743592 | biostudies-literature